Particle Swarm Optimization: A Powerful Technique for Solving Engineering Problems

Almeida, Bruno Seixas Gomes de; Leite, Victor Coppo

doi:10.5772/intechopen.89633

Cited by 42 publications

(17 citation statements)

References 28 publications

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“…Inspired by the behaviour of swarms such as birds' flocks and ant colonies, Particle Swarm optimization is a widespread metaheuristic that Kennedy and Eberhart first developed. It solves complex nonlinear optimization problems with the advantages of having fewer parameters to adjust and an already available literature discussion and community for the parameters that should be adjusted (Almeida 2019). It consists of finding the solution (global best gbest) by comparing and updating an individual's information of position and velocity (personal best pbest)…”

Section: Particle Swarm Optimization Psomentioning

confidence: 99%

In silico prediction of the inhibition of new molecules on SARS-CoV-2 3CL protease by using QSAR: PSOSVR approach

Madani

Benkortbi

Laidi

2023

Braz. J. Chem. Eng.

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Continuous effort is dedicated to clinically and computationally discovering potential drugs for the novel coronavirus-2. Computer-Aided Drug Design CADD is the backbone of drug discovery, and shifting to computational approaches has become necessary. Quantitative Structure–Activity Relationship QSAR is a widely used approach in predicting the activity of potential molecules and is an early step in drug discovery. 3-chymotrypsin-like-proteinase 3CLpro is a highly conserved enzyme in the coronaviruses characterized by its role in the viral replication cycle. Despite the existence of various vaccines, the development of a new drug for SARS-CoV-2 is a necessity to provide cures to patients. In the pursuit of exploring new potential 3CLpro SARS-CoV-2 inhibitors and contributing to the existing literature, this work opted to build and compare three models of QSAR to correlate between the molecules’ structure and their activity: IC 50 through the application of Multiple Linear Regression(MLR), Support Vector Regression(SVR), and Particle Swarm Optimization-SVR algorithms (PSO-SVR). The database contains 71 novel derivatives of ML300which have proven nanomolar activity against the 3CLpro enzyme, and the GA algorithm obtained the representative descriptors. The built models were plotted and compared following various internal and external validation criteria, and applicability domains for each model were determined. The results demonstrated that the PSO-SVR model performed best in predictive ability and robustness, followed by SVR and MLR. These results also suggest that the branching degree 6 had a strong negative impact, while the moment of inertia X/Z ratio, the fraction of rotatable bonds, autocorrelation ATSm2, Keirshape2, and weighted path of length 2 positively impacted the activity. These outcomes prove that the PSO-SVR model is robust and concrete and paves the way for its prediction abilities for future screening of more significant inhibitors' datasets. Supplementary Information The online version contains supplementary material available at 10.1007/s43153-023-00332-z.

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Section: Particle Swarm Optimization Psomentioning

confidence: 99%

In silico prediction of the inhibition of new molecules on SARS-CoV-2 3CL protease by using QSAR: PSOSVR approach

Madani

Benkortbi

Laidi

2023

Braz. J. Chem. Eng.

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“…In this situation, the movement of the birds is a choreography; the birds still fly simultaneously for a time till the best area available for food and security is assured, then the flock lands at the same time. As a consequence, the determination of which location the whole swarm should land is a complicated issue [30]. The steps of the PSO algorithm are shown in Figure 1.…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

Radio-frequency circular integrated inductors sizing optimization using bio-inspired techniques

Hajjami

Benhala

2022

IJECE

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<span lang="EN-US">In this article, a comparative study is accomplished between three of the most used swarm intelligence (SI) techniques; namely artificial bee colony (ABC), ant colony optimization (ACO), and particle swarm optimization (PSO) to carry out the optimal design of radio-frequency (</span><span lang="EN-US">RF) spiral inductors, the three algorithms are applied to the cost function of RF circular inductors for 180 nm beyond 2.50 GHz, the aim is to ensure optimal performance with less error in inductance, and a high-quality factor when compared to electromagnetic simulation. Simulation experiments are achieved and performances regarding convergence velocity, robustness, and computing time are checked. Also, this paper shows an impact study of technological parameters and geometric features on the inductance and the quality factor of the studied integrated inductor. The building method of constraints design with algorithms used has given good results and electromagnetic simulations are of good accuracy with an error of 2.31% and 4.15% on the quality factor and inductance respectively. The simulation shows that ACO provides more accuracy in circuit size and fewer errors than ABC and PSO, while PSO and ABC are better in terms of convergence velocity.</span>

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“…PSO is a metaheuristic optimization algorithm that was developed by Kennedy and Eberhart in 1995, inspired by the behavior of animals like birds and fish, and is very much adapted to solving nonlinear optimization problems [32]. It is an evolutionary algorithm that is based on swarm intelligence.…”

Section: Particle Swarm Optimization (Pso)mentioning

confidence: 99%

Optimal Placement of Electric Vehicle Charging Stations in a Distribution Network With Randomly Distributed Rooftop Photovoltaic Systems

Fokui¹,

Saulo

Ngoo

2021

IEEE Access

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The increasing number of electric vehicles (EVs) in today's transport sector is gradually leading to the phasing out of petroleum-based vehicles. However, the rapid deployment of EVs largely depends on the coordinated and fast expansion of EV charging stations (EVCSs). The integration of EVCSs in the modern distribution network characterized by increased penetration of randomly distributed photovoltaic (PV) systems is challenging as they can lead to excessive power losses and voltage deviations beyond acceptable limits. In this paper, a hybrid bacterial foraging optimization algorithm and particle swarm optimization (BFOA-PSO) technique is proposed for the optimal placement of EVCSs into the distribution network with high penetration of randomly distributed rooftop PV systems. The optimization problem is formulated as a multi-objective problem minimizing active and reactive power losses, average voltage deviation index, and maximizing voltage stability index. The IEEE 69 node distribution network is used as the case network. The simulation is done using MATLAB to integrate the EVCSs in five cases of randomly sized and placed PV systems in the distribution network. For all five cases, a minimal increase in power losses is recorded with minor changes in the voltage deviation and stability indices due to the placement of the EVCSs. But for the voltages of nodes 29 to 48, the other node voltages remain unchanged upon placement of the EVCSs. The largest increase in power losses due to the EVCSs being brought into the network with PVs was noticed in case 3 (from 142.27kW, and 62.90kVar to 147.65kW, and 72.48kVar).

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Particle Swarm Optimization: A Powerful Technique for Solving Engineering Problems

Cited by 42 publications

References 28 publications

In silico prediction of the inhibition of new molecules on SARS-CoV-2 3CL protease by using QSAR: PSOSVR approach

In silico prediction of the inhibition of new molecules on SARS-CoV-2 3CL protease by using QSAR: PSOSVR approach

Radio-frequency circular integrated inductors sizing optimization using bio-inspired techniques

Optimal Placement of Electric Vehicle Charging Stations in a Distribution Network With Randomly Distributed Rooftop Photovoltaic Systems

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